Dynamic, adaptable enterprise ontologies
Model business reality as it is, not how a software vendor’s schema dictates.
An ontology is the conceptual blueprint of your business. JanusKS creates extensible, domain-specific semantic models that describe entities, properties and constraints in plain business terminology.
Schema decoupling
Keep underlying operational tools (SAP, Primavera, Procore, Salesforce) completely isolated. If a source tool updates its table structure, your core ontology stays stable.
Domain adaptability
Upper and domain ontologies tailored out of the box for Building & Construction, Healthcare, Heavy Industry, Banking & Finance and Media Production.
Living semantic models
Add new assets, contractor categories or statutory constraints on the fly, without database downtime or breaking downstream analytics.
High-performance graph engine
Traverse deep relational networks at computational speed.
Our graph architecture is built from the ground up for low-latency operational traversal, handling massive datasets without the scaling bottlenecks of legacy relational databases.
Sub-second querying
Multi-hop graph queries across billions of nodes and relational edges.
Native property graph
Rich metadata, confidence intervals and spatial coordinates sit directly on vertices and edges.
Scalable and distributed
Engineered for hybrid cloud, private infrastructure and air-gapped local clusters.
Deterministic inference and anomaly surfacing
A static database only answers what is explicitly written. The JanusKS semantic inference engine evaluates rules, transitive relationships and constraints to generate new operational intelligence automatically.
Transitive dependency mapping
Links chains of requirements end to end, so a problem at one end surfaces against the milestone at the other, as in the example below.
Constraint-satisfaction checking
Automatically surfaces any operational configuration that breaches statutory hold points, mandatory safety stand-downs or contractual SLA buffers.
Semantic deduplication and entity resolution
Links disparate source references to the same real-world asset, tool or person in one unified, definitive graph node.
# Example: a hidden bottleneck, found automatically
Switchgear commissioning requires Specialist Technician A
Specialist Technician A requires HV switching certification
HV switching certification expires next Tuesday
# Result: the commissioning milestone is flaggedVerified grounding for agentic AI and LLMs (Graph RAG)
Give enterprise AI the verified context to plan, simulate and decide.
Standard large language models generate text from statistical probabilities and can hallucinate critical facts. JanusKS acts as the deterministic context engine for modern enterprise AI.
Graph-augmented retrieval
Ground AI prompts with structured, factual knowledge sub-graphs rather than noisy, uncurated document chunks.
Native MCP integration
Let external models (Claude, ChatGPT, Gemini, Copilot, Cursor) navigate your operational graph through secure, structured MCP tool calls.
Explainable reasoning chains
Every recommendation from an AI agent can be audited back through the exact path of graph nodes and relations behind it.
Relational data vs the JanusKS knowledge graph
| Evaluation vector | Relational databases and data warehouses | JanusKS Enterprise Knowledge Graph |
|---|---|---|
| Data representation | Flat, rigid tables, foreign keys, isolated silos. | Rich semantic network of interconnected entities, properties and relationships. |
| Handling complexity | Slows sharply as multi-table JOIN operations stack up. | Constant-time pointer hopping at each step of a deep, multi-hop relationship chain. |
| Schema flexibility | Brittle; needs schema migrations and ETL pipeline rewrites. | Dynamic; introduces new entities and relations without altering the core structure. |
| Intelligence layer | Passive; stores numbers and strings without embedded context. | Active; infers unstated relationships and enforces business rules mathematically. |
| AI compatibility | Poor; vector search alone leaves models prone to hallucination. | Optimal; provides deterministic ground truth for agentic AI through Graph RAG. |
Enterprise security, governance and deployment
Your enterprise ontology and semantic relationships encode your organisation’s most valuable intellectual property: its operational trade secrets, formulas and supplier relationships.
Node and edge-level access control
Fine-grained attribute-based access control (ABAC) sets exactly which nodes, such as executive compensation or sensitive supplier pricing formulas, specific users or autonomous agents can traverse.
Immutable temporal ledger
Track every structural addition, relationship change and node deletion over time for regulatory compliance and forensic audit.
Air-gapped and local inference
Deploy the entire graph engine behind your corporate firewall on bare metal or in private VPCs, isolated from public cloud dependencies.